De-noising and retrieving algorithm of Mie lidar data based on the particle filter and the Fernald method.
De-noising and retrieving algorithm of Mie lidar data based on the particle filter and the Fernald method.
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DOI：
10.1364/oe.23.026509
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发表时间：
2015-10
期刊：
影响因子：
3.8
通讯作者：
Chen Li;Zengxin Pan;Feiyue Mao;W. Gong;Shihua Chen;Q. Min
中科院分区：
文献类型：
--
作者：
Chen Li;Zengxin Pan;Feiyue Mao;W. Gong;Shihua Chen;Q. Min
大气激光雷达的信噪比（SNR）随着距离的增加而迅速降低，因此在远端检索激光雷达数据时难以保持较高的精度。为了避免这个问题，人们开发了许多去噪算法；特别地，提出了一种结合集合卡尔曼滤波（EnKF）和费尔纳德方法的有效去噪算法，可以同时检索激光雷达数据并获得去噪信号。该算法提高了基于费尔纳德方法的激光雷达的检索精度和有效测量范围，但有时会由于EnKF引起的过度平滑而导致近距离偏移（偏差）。本研究提出了一种新的方案来避免这种现象，在去噪算法中使用粒子滤波器（PF）代替EnKF。综合实验表明，该方法的性能优于EnKF和Fernald方法。PF方法的均方根误差分别为Fernald和EnKF方法的52.55%和38.14%，信噪比分别比Fernald和EnKF方法提高了44.36%和11.57%。对于真实信号的实验，EnKF的相对偏置为5.72%，PF在近距离内将其降低到2.15%。此外，PF方法对远范围随机噪声的抑制作用也非常显著。广泛应用PF方法可用于确定气溶胶的局部和全局特性。
The signal-to-noise ratio (SNR) of an atmospheric lidar decreases rapidly as range increases, so that maintaining high accuracy when retrieving lidar data at the far end is difficult. To avoid this problem, many de-noising algorithms have been developed; in particular, an effective de-noising algorithm has been proposed to simultaneously retrieve lidar data and obtain a de-noised signal by combining the ensemble Kalman filter (EnKF) and the Fernald method. This algorithm enhances the retrieval accuracy and effective measure range of a lidar based on the Fernald method, but sometimes leads to a shift (bias) in the near range as a result of the over-smoothing caused by the EnKF. This study proposes a new scheme that avoids this phenomenon using a particle filter (PF) instead of the EnKF in the de-noising algorithm. Synthetic experiments show that the PF performs better than the EnKF and Fernald methods. The root mean square error of PF are 52.55% and 38.14% of that of the Fernald and EnKF methods, and PF increases the SNR by 44.36% and 11.57% of that of the Fernald and EnKF methods, respectively. For experiments with real signals, the relative bias of the EnKF is 5.72%, which is reduced to 2.15% by the PF in the near range. Furthermore, the suppression impact on the random noise in the far range is also made significant via the PF. An extensive application of the PF method can be useful in determining the local and global properties of aerosols.